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Field
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effects and their coupled interactions. In effect, this a complex problem that needs the application for AI / machine learning to enable guided, efficient and effective optimization of the CHIPLET
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an environment that is diverse, inclusive and respectful. Learn more about our lab here: https://bioniclab.seas.harvard.edu/ We are recruiting fellows from diverse backgrounds interested in solving tough problems
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record and familiarity with the existing literature and research in the field of statistical machine learning, and deep learning or large language models. Possess sufficient specialist knowledge, research
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research teams; and Knowledge of Artificial Intelligence/Machine Learning (AI/ML). Instructions to Applicants: For full consideration, applicants must apply for Postdoctoral Research Fellow at https
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learning or large language models. Strong publication record and familiarity with the existing literature and research in the field of statistical machine learning, and deep learning or large language models
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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You Completion (level A and B) or near completion (level A) of a PhD in the field of Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data. Demonstrated expert
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technology. Key Responsibilities: Collaborate with partners from both the academia and the industry to lead and/or conduct innovative research on, but not limited to transfer learning, explainable machine
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, computer scientists, and policy experts. The postdocs will work with Prof. Daniel Loveless on various areas of research, including radiation and reliability effects in emerging semiconductor technologies
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be